
Difference to noisy baseline (\(INFD_{NB}\) ): \(\mathbf {I} = \mathbf {x} - \epsilon \) , where \(\epsilon \sim \mathcal {N}(0, \sigma ^2)\) . This corresponds to a robust variant of the completeness axiom [1]}, where we take a Gaussian random vector centered around a zero baseline, instead of a constant zero baseline.

Square removal (\(INFD_{SQ}\) ): in this case, \(\mathbf {I}\)  has a uniform distribution over square patches of the image \(\mathbf {x}\)  of some predefined size. This can better capture spatial relationships in the images, as the removal of single pixels actually removes very little information if the surrounding pixels are still intact.

